Deblurring for spatial and temporal varying motion with optical computing

Xiao Xiao; Dongfeng Xue; Zhao Hui

doi:10.1117/1.OE.55.5.053103

5 May 2016 Deblurring for spatial and temporal varying motion with optical computing

Xiao Xiao, Dongfeng Xue, Zhao Hui

Author Affiliations +

Funded by: Central Universities in Xidian University, Natural Science Foundation of China, Fund of the State Key Laboratory of Rare Earth Resource Utilization

Optical Engineering, Vol. 55, Issue 5, 053103 (May 2016). https://doi.org/10.1117/1.OE.55.5.053103

Abstract

A way to estimate and remove spatially and temporally varying motion blur is proposed, which is based on an optical computing system. The translation and rotation motion can be independently estimated from the joint transform correlator (JTC) system without iterative optimization. The inspiration comes from the fact that the JTC system is immune to rotation motion in a Cartesian coordinate system. The work scheme of the JTC system is designed to keep switching between the Cartesian coordinate system and polar coordinate system in different time intervals with the ping-pang handover. In the ping interval, the JTC system works in the Cartesian coordinate system to obtain a translation motion vector with optical computing speed. In the pang interval, the JTC system works in the polar coordinate system. The rotation motion is transformed to the translation motion through coordinate transformation. Then the rotation motion vector can also be obtained from JTC instantaneously. To deal with continuous spatially variant motion blur, submotion vectors based on the projective motion path blur model are proposed. The submotion vectors model is more effective and accurate at modeling spatially variant motion blur than conventional methods. The simulation and real experiment results demonstrate its overall effectiveness.

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Xiao Xiao, Dongfeng Xue, and Zhao Hui "Deblurring for spatial and temporal varying motion with optical computing," Optical Engineering 55(5), 053103 (5 May 2016). https://doi.org/10.1117/1.OE.55.5.053103

Published: 5 May 2016

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KEYWORDS

Cameras

Motion models

Computing systems

Optical computing

Motion estimation

Point spread functions

Motion measurement

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